AI Engineer (Artificial Intelligence Engineer)
Leesburg, VA · HybridFull-timeSeen 5 days agoSeen in employer's feed 5 days ago
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Job overview
The AI/ML Engineer develops and operationalizes AI solutions for enterprise and Government applications, including model workflows, generative AI, and large language models. The role evaluates models and outputs, supports AI red-team testing, and communicates architecture, performance, risks, and recommendations to technical and non-technical stakeholders. The position is hybrid, with two office days per week in Reston, Virginia, and requires U.S. citizenship and eligibility for a Federal Government security clearance.
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Full job description
AI/ML Engineer Full-Time Salaried Role Hybrid Role - 2 days in office, on Tuesdays and Thursdays in Reston, VA U.S. Citizenship is required. Candidate must be eligible to obtain and maintain a Federal Government security clearance. Ability to travel up to 50% of the time. Description Lead or contribute to cross-functional teams to develop and operationalize AI solutions that help solve our most challenging problems. - Apply modern engineering techniques to design, develop, deploy and maintain end-to-end AI workflows spanning model training, inference and performance monitoring. - Adapt and integrate diverse AI model architectures, including computer vision systems, natural language processors, audio processors, large language models (LLMs) and multi-modal frameworks to address complex mission-critical challenges. - Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency and address these challenges through regular fine-tuning to ensure continued alignment with evolving mission needs and organizational goals. - Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications. - Test and evaluate AI solutions against mission requirements and produce actionable recommendations. Job responsibilities Design, develop, train, test, deploy, and maintain AI and machine learning models for enterprise-scale and Government applications. Develop and operationalize production of AI/ML solutions, ensuring models and supporting systems are reliable, scalable, maintainable, and appropriate for VA mission requirements. Collect, clean, transform, analyze, and prepare structured and unstructured data for AI/ML model development, training, testing, and evaluation. Evaluate potential AI technologies, models, tools, frameworks, and architectures and recommend solutions appropriate for complex Federal Government and cybersecurity use cases. Apply AI and machine learning techniques to solve real-world business, cybersecurity, engineering, analytics, and operational problems across a large enterprise environment. Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other model controls. Evaluate AI-generated outputs for accuracy, factuality, grounding, reliability, bias, helpfulness, honesty, and overall model performance before outputs are incorporated into VA efforts. Design and execute AI/ML model evaluation and validation methodologies, including benchmark testing and comparative evaluation of model outputs. Perform and support AI red-team testing to identify weaknesses, unintended model behaviors, bias, security vulnerabilities, and other risks associated with AI-generated outputs. Implement mechanisms and controls to improve the factuality and grounding of AI/LLM outputs, including system-level instructions, source attribution, content controls, and appropriate response behavior when information is incomplete, contradictory, or uncertain. Produce technical documentation, analysis, recommendations, briefings, and other materials that clearly communicate AI architecture, model performance, risks, controls, and recommended courses of action to both technical and non-technical stakeholders. Deliver Government-owned AI-related technical artifacts developed under the program, including applicable models, model weights, algorithms, configurations, documentation, data models, source code, and supporting technical compo
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